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In an earlier post, I introduced the golden section search method – a modification of the bisection method for numerical optimization that saves computation time by using the golden ratio to set its test points.  This post contains the R function that implements this method, the R functions that contain the 3 functions that were minimized by this method, and the R script that ran the minimization.

I learned some new R functions while learning this new algorithm.

– the curve() function for plotting curves

– the cat() function for concatenating strings and variables and, hence, for printing debugging statements

As usual, I made sure to change the working directory in R to the folder that contains all of these files, and I used the source() function to call the relevant functions in my script for running everything.

Here is the function that I minimized in my earlier blog post; I titled it “f.R”.

```f = function(x)
{
abs(x - 2) + (x - 1)^2
}```

Here is the function that implemented the golden section search method; I called it “golden.section.search.R”.

```##### Implementing the golden section search method
##### a modification of the bisection method with the golden ratio
##### By Eric Cai - The Chemical Statistician
golden.section.search = function(f, lower.bound, upper.bound, tolerance)
{
golden.ratio = 2/(sqrt(5) + 1)

### Use the golden ratio to set the initial test points
x1 = upper.bound - golden.ratio*(upper.bound - lower.bound)
x2 = lower.bound + golden.ratio*(upper.bound - lower.bound)

### Evaluate the function at the test points
f1 = f(x1)
f2 = f(x2)

iteration = 0

while (abs(upper.bound - lower.bound) > tolerance)
{
iteration = iteration + 1
cat('', '\n')
cat('Iteration #', iteration, '\n')
cat('f1 =', f1, '\n')
cat('f2 =', f2, '\n')

if (f2 > f1)
# then the minimum is to the left of x2
# let x2 be the new upper bound
# let x1 be the new upper test point
{
cat('f2 > f1', '\n')
### Set the new upper bound
upper.bound = x2
cat('New Upper Bound =', upper.bound, '\n')
cat('New Lower Bound =', lower.bound, '\n')
### Set the new upper test point
### Use the special result of the golden ratio
x2 = x1
cat('New Upper Test Point = ', x2, '\n')
f2 = f1

### Set the new lower test point
x1 = upper.bound - golden.ratio*(upper.bound - lower.bound)
cat('New Lower Test Point = ', x1, '\n')
f1 = f(x1)
}
else
{
cat('f2 < f1', '\n')
# the minimum is to the right of x1
# let x1 be the new lower bound
# let x2 be the new lower test point

### Set the new lower bound
lower.bound = x1
cat('New Upper Bound =', upper.bound, '\n')
cat('New Lower Bound =', lower.bound, '\n')

### Set the new lower test point
x1 = x2
cat('New Lower Test Point = ', x1, '\n')

f1 = f2

### Set the new upper test point
x2 = lower.bound + golden.ratio*(upper.bound - lower.bound)
cat('New Upper Test Point = ', x2, '\n')
f2 = f(x2)
}
}

### Use the mid-point of the final interval as the estimate of the optimzer
cat('', '\n')
cat('Final Lower Bound =', lower.bound, '\n')
cat('Final Upper Bound =', upper.bound, '\n')
estimated.minimizer = (lower.bound + upper.bound)/2
cat('Estimated Minimizer =', estimated.minimizer, '\n')
}```

Here is the script that ran everything; I called it “minimization.R”.

```##### Finding the minimizers of functions using the bisection method with the golden ratio
##### By Eric Cai - The Chemical Statistician

# Calling the user-defined functions in the working directory
source('golden.section.search.R')
source('f.R')

# printing the PNG images into the working directory
png('INSERT YOUR DIRECTORY PATH HERE/cusped function.png')
# plotting the curve of my user-defined function
curve(f, from = 1, to = 3, main = expression(paste('f(x) = |x - 2| + (x - 1)'^'2')))
dev.off()

# finding the minimizer of my user-defined function using my golden bisection method
golden.section.search(f, 1, 3, 1e-5)```

Filed under: Applied Mathematics, Numerical Analysis, Plots, R programming, Statistical Computing Tagged: applied mathematics, numerical analysis, numerical method, numerical methods, numerical optimization, optimization, R, R programming, statistical computing  